no code implementations • EMNLP (NLP4ConvAI) 2021 • Subhadarshi Panda, Caglar Tirkaz, Tobias Falke, Patrick Lehnen
As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language.
no code implementations • COLING 2020 • Shailza Jolly, Tobias Falke, Caglar Tirkaz, Daniil Sorokin
Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling.
no code implementations • COLING 2020 • Tobias Falke, Markus Boese, Daniil Sorokin, Caglar Tirkaz, Patrick Lehnen
In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives.
no code implementations • 15 Nov 2019 • Shubham Kapoor, Caglar Tirkaz
A common approach that is adapted in digital assistants when responding to a user query is to process the input in a pipeline manner where the first task is to predict the domain, followed by the inference of intent and slots.
1 code implementation • 13 Feb 2017 • Eray Yildiz, Caglar Tirkaz, H. Bahadir Sahin, Mustafa Tolga Eren, Ozan Sonmez
In this paper, we propose a system that uses deep learning techniques for morphological disambiguation.
no code implementations • 8 Feb 2017 • H. Bahadir Sahin, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren, Ozan Sonmez
Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia.